1. Field of the Invention
The present invention is directed to a method for the classification of field patterns that are generated by electrophysiological activities occurring inside the body of a living subject and which are measured outside the subject with a measuring apparatus, having an adaptive "teachable" classifier that is connected to the measuring apparatus.
2. Description of the Prior Art
U.S. Pat. No. 5,092,343 discloses a method of the type generally described above. An electromyographic signal, for example, taken from a muscle via a needle, is sampled and searched for typical signal shapes. The typical signal shapes that are recognized are then supplied to means for classification that allocate the typical signal shapes to pathological conditions. The means for classification comprise a neural network that is trained with data of normal groups and patient groups.
An article by Pfurtscheller et al., "Sleep Classification in Infants Based on Artificial Neural Networks", in Biomedizinische Technik, Vol. 37, No. 6/1992, pages 122-130 discloses the formation of data vectors, that are evaluated with the assistance of two neural networks, for sleep classification from polygraphic data, i.e. data of a plurality of events and phenomena such as EEG, EOG, EMG, ECG, etc. The neural networks were previously trained with classifications entered by a person who is knowledgeable in this field.
A measuring apparatus with which spatial field patterns of the type described above can be measured is disclosed in European Application 0 359 864. The measuring apparatus is a multi-channel measuring apparatus and is also referred to as biomagnetic measuring system. The field patterns of extremely weak magnetic fields that are generated by electrophysical activities occurring inside the body of a living subject can thus be measured. The multi-channel measuring apparatus includes a multi-channel gradiometer arrangement that is coupled to a multi-channel SQUID arrangement. The signals are measured at spatially separate locations at identical times with the multi-channel measuring apparatus, and combine to form the field patterns, which are utilized to construct a surrogate model of the electrophysiological activity. A sphere and an infinite half-space of uniform conductivity wherein a source of magnetic signals is located are used therein as the surrogate model for activities.
Both magnetoencephalograms (MEG) and magneto cardiograms (MCG) can be measured with the biomagnetic measuring system. The principal goal for the evaluation of the MEG or MCG recordings is a three-dimensional, non-invasive localization of sources of pathological electrophysiological activities. To that end, that pathological activities or events must be identified from the MEG or MCG registrations in a first step. Pathological events that occur in the cerebrum are, for example, spike activity, steep waves, slow waves, K-complexes or rhythmic activity in the theta or delta region.
The registration duration is on the order of magnitude of 5 through 10 minutes, which corresponds to approximately 30 through 60 pages of registration. A skilled physician requires approximately 10 seconds for identifying the pathological events for one page, which is in the form of a 30 cm long paper recording or as an image on a computer picture screen. This means that approximately 30 through 60 "pages" must be searched for pathological events. This work is generally extremely time-intensive and exhausting. Important data sections can also be very easily overlooked.
However, all selected pathological activities or events cannot be located with the same reliability or precision. Thus, for example, the electrophysiological activity must be capable of being represented by a model. The selection of the events that are utilized for source localization also substantially influences the reliability of the localization, whereby the signal-to-noise ratio is critical. When similar events that follow one another are averaged, the success of the event recognition is dependent on a selected comparison result with which the events to be averaged are identified.